Hyperspectral Imagery for Environmental Mapping and Monitoring: Case Study of Grassland in Belgium. The objective of this study is to show that hyperspectral imagery can be used to characterise grassland as well as its biophysical and biochemical properties.

1. Hyperspectral Imagery forHyperspectral Imagery for
Environmental Mapping and MonitoringEnvironmental Mapping and Monitoring
Case Study of Grassland in BelgiumCase Study of Grassland in Belgium
Biometry, Data management and Agrometeorology Unit
buffet@cra.wallonie.be – oger@cra.wallonie.be
CENTRE WALLON DE RECHERCHES AGRONOMIQUES

2. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
ContextContext
The objective of this study is to show that hyperspectral imagery
can be used to characterise grassland as well as its biophysical
and biochemical properties.
 Inventory of forage production & qualityInventory of forage production & quality
 Management practicesManagement practices
 Control of application of agri-environmental measuresControl of application of agri-environmental measures
Grassland is an important component of the agricultural landscape.
Monitoring grassland at the regional level is closely linked to the
knowledge of regional:

3. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
PresentationPresentation
 Scientific objectivesScientific objectives
 MaterialMaterial

Study areaStudy area

Field campaign (before flight)Field campaign (before flight)

Field campaign (during flight)Field campaign (during flight)
 MethodMethod

Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel)

Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)
 ResultsResults
 ConclusionsConclusions

4. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Scientific objectivesScientific objectives
 Agri-Environmental Measures (AEM):
• Now compulsory (CAP reform, WR regulations)
• Temporal constraints (Specific cutting or grazing periods)
• Opportunity of using airborne imaging spectroscopy to trace the
recent history of grasslands in terms of managements
 Working hypothesis: cutting or grazing actions
• Can be considered as major stresses, which have an impact on the
spectral signatures of grass canopy.
• Changes of spectral signature depend not only on the nature and
the intensity of the action, but also on the time spent between the
action and the remote sensing data acquisition.

5. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Scientific objectivesScientific objectives
Several campaigns with CASI/SASI, CASI-ATM airborneSeveral campaigns with CASI/SASI, CASI-ATM airborne
sensorssensors
 A continuum and a possible validation of the first campaign:
• CASI-2 (VIS/NIR) and SASI (SWIR) sensor
• August 2002
• Lorraine test site
• Biophysical (wet matter, biomass, grass height…)
• Biochemical (protein, VEM, DVE…)
 Tries to enlighten how imaging spectroscopy are capable to:
• Determine grassland management practices
• Control of application of Agri-Environmental Measures

6. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
PresentationPresentation
 Scientific objectivesScientific objectives
 MaterialMaterial

Study areaStudy area

Field campaign (before flight)Field campaign (before flight)

Field campaign (during flight)Field campaign (during flight)
 MethodMethod

Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel)

Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)
 ResultsResults
 ConclusionsConclusions

7. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Study areasStudy areas
 South-east of Belgium.South-east of Belgium.
 Luxembourg ProvinceLuxembourg Province
(Belgian Lorraine).(Belgian Lorraine).
 Grassland > 75% of land-Grassland > 75% of land-
use.use.
 Study area corners:Study area corners:
 Study area =Study area = ±±50 Km²50 Km²
 10 flight lines with a rate of10 flight lines with a rate of
covering of 40%.covering of 40%.
Lat. Long
ULC 49°43' 23.16 N 5°27' 34.60 E
LRC 49°39' 00.43 N 5°31' 12.97 E

8. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Study areasStudy areas
 South-east of Belgium.South-east of Belgium.
Luxembourg Province (near Arlon).Luxembourg Province (near Arlon).
 Located in Natura 2000.Located in Natura 2000.
 Grassland > 75% of land-useGrassland > 75% of land-use
 Study area =Study area = ±±35 Km²35 Km²
 A subset of 33 parcels was followed:A subset of 33 parcels was followed:

Ground cover was scored visually from theGround cover was scored visually from the
middle of May to the airborne flight.middle of May to the airborne flight.

Biophysical parameters (GH, FMY, DMY…)Biophysical parameters (GH, FMY, DMY…)

Field spectroradiometer (ASD)Field spectroradiometer (ASD)

9. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Field campaignField campaign
Biophysical parametersBiophysical parameters
 17 parcels17 parcels
 4 sampling units / parcel4 sampling units / parcel
 During airborne flightDuring airborne flight
 2 types of samples2 types of samples

Mower cutting: total canopyMower cutting: total canopy

Scissors cutting: upper canopyScissors cutting: upper canopy
 Biophysical parametersBiophysical parameters

Grass heightGrass height

Grass biomass (wet matter)Grass biomass (wet matter)
 Field spectroradiometersField spectroradiometers

ASD FieldSpec Pro FrASD FieldSpec Pro Fr

GER 1500GER 1500

10. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Field campaign (before flight)Field campaign (before flight)
PARCEL OBS1 OBS2 OBS3 OBS4 OBS5 OBS6 OBS7 OBS8 OBS9 OBS10 OBS11 MCo LAST CUT
1 * P RP P P P RP RP P RP F P 22/06/2004
2 P P P P P P P P P P F P 22/06/2004
3 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/2004
4 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/2004
5 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/2004
6 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 15/06/2004
7 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF
8 * P P P P P P P P P P P
9 NP NF NP NF NP NF F RF RF RF RF RF RF RF F 28/05/2004
10 P P R R R P P P P P RP P
11 RF RF RF P P P RP P P P P P
12 NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 09/06/2004
13 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F F 22/06/2004
14 NP NF NP NF NP NF F RF RF RF RF RF RF RF F 27/05/2004
15 NP NF NP NF NP NF F RF RF RF RF RF P RP F 28/05/2004
16 * P P P P P P P P P P PP
17 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/2004
18 * NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 07/06/2004
19 * P P P PR P P RP RP RP RP PP
20 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF
21 * P P P PR P P P P P RP PP
22 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF
23 P P RP P P P P P P P P PP
24 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/2004
25 NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 07/06/2004
26 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/2004
27 * NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF RF F 14/06/2004
28 * NP NF P P P P P P P P P P
29 * NP NF NP NF NP NF NP NF NP NF NP NF F RF RF RF F 10/06/2004
30 * P P P P P P P P P P PP
31 * P P P P P P P P RP RP PP
32 * NP NF NP NF NP NF NP NF NP NF F RF RF RF RF F 09/06/2004
33 NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF NP NF F RF F 18/06/2004
Table 1. Management practices observed on the 33 monitored parcels and their respective observed
management class (MCo). (R = Restoration, F = Haying, P = grazing, NP NF = No Grazing and No Haying).

11. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectroradiometric imagersSpectroradiometric imagers
CASI – SASI and satellite sensors bandwidth comparison
 CASI-2CASI-2 ((Compact Airborne Spectrographic ImagerCompact Airborne Spectrographic Imager).).

VIS-NIR (400-950 nm)VIS-NIR (400-950 nm)

# spectral bands: 96 bands# spectral bands: 96 bands

Spectral resolution: 5.7 nmSpectral resolution: 5.7 nm

Spatial resolution: 2.5 x 2.5 mSpatial resolution: 2.5 x 2.5 m

Swath width: 303 pixelsSwath width: 303 pixels
Contiguous narrowbands

12. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectroradiometric imagersSpectroradiometric imagers
CASI – SASI and satellite sensors bandwidth comparison
 SASISASI ((Shortwave Infrared Airborne Spectrographic ImagerShortwave Infrared Airborne Spectrographic Imager))

SWIR (850-2450 nm)SWIR (850-2450 nm)

# spectral bands: 160 bands# spectral bands: 160 bands

Spectral resolution: 10 nmSpectral resolution: 10 nm

Spatial resolution: 2 x 2 mSpatial resolution: 2 x 2 m

Swath width: 600 pixelsSwath width: 600 pixels

13. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Hyperspectral data acquisitionHyperspectral data acquisition
 End of JuneEnd of June
 CASI resolution:CASI resolution:
- 2.4 x 2.4 m- 2.4 x 2.4 m
-
96 spectral bands96 spectral bands
 10 transects by sensors10 transects by sensors
 Quality checkQuality check (signal & geometric)(signal & geometric)
Bad weather conditionsBad weather conditions
• Signal problemsSignal problems
• Image correctionImage correction
=> 24 parcels were preserved=> 24 parcels were preserved 0
100
200
300
400
500
600
700
800
0.45
0.47
0.5
0.52
0.54
0.56
0.59
0.61
0.63
0.65
0.68
0.7
0.72
0.75
0.77
0.79
0.82
0.84
0.86
0.88
9_Corr
9_Brute
9_Terrain
0
100
200
300
400
500
600
0.45
0.47
0.5
0.52
0.54
0.56
0.59
0.61
0.63
0.65
0.68
0.7
0.72
0.75
0.77
0.79
0.82
0.84
0.86
0.88
7_brute
7_Terrain

14. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Hyperspectral data acquisitionHyperspectral data acquisition
 10 transects by sensors10 transects by sensors
 Level II bLevel II b
 Quality checkQuality check
(signal & geometric)(signal & geometric)

SASI = some problemsSASI = some problems
 3x3 pixel subset3x3 pixel subset
centred over thecentred over the
sampling unit locationsampling unit location
SASI – 1345 nm

15. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Laboratory analysisLaboratory analysis
Biochemical parametersBiochemical parameters
 Post field campaignPost field campaign
 NIR Spectrometry laboratory-based measurementsNIR Spectrometry laboratory-based measurements

Samples cut with scissors & mowerSamples cut with scissors & mower

Fresh samples & dry samplesFresh samples & dry samples
 Biochemical parameters measuredBiochemical parameters measured

Dry matter contentDry matter content

Proteins contentProteins content

Cellulose contentCellulose content

Ashes contentAshes content

Sugar contentSugar content

Energy values which characterised available energy for milkEnergy values which characterised available energy for milk
and meat production: (VEM, VEVI, DVE and OEB)and meat production: (VEM, VEVI, DVE and OEB)

16. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
PresentationPresentation
 Scientific objectivesScientific objectives
 MaterialMaterial

Study areaStudy area

Field campaign (before flight)Field campaign (before flight)

Field campaign (during flight)Field campaign (during flight)
 MethodMethod

Spectral analysis at Pixel level (intra-parcel)Spectral analysis at Pixel level (intra-parcel)

Spectral analysis at parcel level (inter-parcel)Spectral analysis at parcel level (inter-parcel)
 ResultsResults
 ConclusionsConclusions

17. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Methodology : General overviewMethodology : General overview
CASI & SASI
Images
Pixel
spectral
responses
Moving average
+
First derivative
biophysical or
biochemical
variable
Correlogram
Spectral
indices
Predictive
models
Selected
narrowbands
biophysical or
biochemical
variable

18. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
Wavelength (nm)
CASI Sensor SASI Sensor
400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250 1300 1350 1400 1450 1500
MSC
MPTC
CELC
SSTC
VEMC
VEVIC
DVEC
OEBC
Hauteur
Biomasse
MF/Ha
MSC
MPTC
CELC
SSTC
VEMC
VEVIC
DVEC
OEBC
Hauteur
Biomasse
MF/Ha
1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300 2350 2400
Dry matter
Protein
Cellulose
Sugar
VEM
VEVI
DVE
OEB
Height
Biomass
Wet matter
Dry matter
Protein
Cellulose
Sugar
VEM
VEVI
DVE
OEB
Height
Biomass
Wet matter
Reflectances
First derivatives
Reflectance
Bare soilJust harvested grassland
Not harvested
Identification of best narrow-wavebandsIdentification of best narrow-wavebands (CASI + SASI)(CASI + SASI)

19. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Step 2: Spectral indicesStep 2: Spectral indices (cont.)(cont.)
 Photochemical Reflectance Index (PRI):
 Red-edge slope, Red-edge step and Red-edge maximum
slope wavelength:
569529
569529
RR
RR
PRI
+
−
=
nm780to677rangein themax
λλ






=
d
dR
RESL
677780 RRREST −=
RESLREMS λ=
This index is related to the green-edge narrowband

20. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Step 2: Spectral indicesStep 2: Spectral indices (cont.)(cont.)
 Water Band Index (WBI) :
 Normalized Difference Vegetation
Index (NDVI):
This index takes advantage of the water absorption
features and represents canopy water content.
Two reflectance ratios were calculated.
677780
677780
RR
RR
NDVI
+
−
=
( )
971
832902902
1
R
RRR
WBI
−+
=
785902
785902
2
λλ −
−
=
RR
WBI
Reflectance indices
0
100
200
300
400
500
600
700
800
400 500 600 700 800 900 1000
Wavelength (nm)
Reflectance
PRI
NDVI
Red-edge Step
& Slope
WBI1 & WBI2

21. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Pixel levelSpectral analysis at Pixel level
Table 2.Table 2. Observed correlation coefficients between the different indices andObserved correlation coefficients between the different indices and
grassland characteristicsgrassland characteristics ..
Leaf water
content
FMY DMY Grass
Height
Biomass
λRESL 0.297 0.078 0.023 0.19 0.087
NDVI 0.814 0.753 0.303 0.417 0.585
PRI 0.716 0.567 -0.016 0.243 0.029
RESL 0.579 0.637 0.215 0.149 0.172
REST 0.55 0.595 0.195 0.127 0.152
WBI1 -0.688 -0.587 -0.163 -0.641 -0.335
WBI2 -0.722 -0.504 -0.074 -0.534 -0.123
 Leaf water content is more highly correlated with spectral reflectance thanLeaf water content is more highly correlated with spectral reflectance than
either total wet biomass or total dry biomass.either total wet biomass or total dry biomass.
Results show that ground truth data collection or canopy sampling for remote sensing studiesResults show that ground truth data collection or canopy sampling for remote sensing studies
of grass canopies should measure the total wet biomass and the total dry biomass. This willof grass canopies should measure the total wet biomass and the total dry biomass. This will
allow for the calculation of the leaf water (also Compton conclusions).allow for the calculation of the leaf water (also Compton conclusions).

22. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Step 3: Multiple regression analysisStep 3: Multiple regression analysis
CASI & SASI
Images
Pixel
spectral
responses
Moving average
+
First derivative
biophysical or
biochemical
variable
Correlogram
Spectral
indices
Predictive
models
Selected
narrowbands
biophysical or
biochemical
variable

23. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Pixel levelSpectral analysis at Pixel level
 This spectral analysis at pixel level is used to investigate and validateThis spectral analysis at pixel level is used to investigate and validate
CASI/SASI-2002 results, with regards to the characterisation of grassCASI/SASI-2002 results, with regards to the characterisation of grass
canopy with quantitative information regarding biophysicalcanopy with quantitative information regarding biophysical
parameters (FMY, DMY, GH).parameters (FMY, DMY, GH).
 CASI-ATM pixel responses were average within 3x3 pixel subsetCASI-ATM pixel responses were average within 3x3 pixel subset
centered around the sampling unit.centered around the sampling unit.
 In addition to the standard channels a number of channel ratios andIn addition to the standard channels a number of channel ratios and
normalized channel difference indices were developed.normalized channel difference indices were developed.

Photochemical Reflectance Index (Photochemical Reflectance Index (PRIPRI).).

Red-edge slope (Red-edge slope (RESLRESL), Red-edge step (), Red-edge step (RESTREST) and Red-edge maximum) and Red-edge maximum
slope wavelength (slope wavelength (REMSREMS).).

Water Band Index (Water Band Index (WBIWBI))

Normalized Difference Vegetation Index (Normalized Difference Vegetation Index (NDVINDVI).).

……

24. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Pixel levelSpectral analysis at Pixel level
Table 2.Table 2. Observed correlation coefficients between the different indices andObserved correlation coefficients between the different indices and
grassland characteristicsgrassland characteristics ..
Leaf water
content
FMY DMY Grass
Height
Biomass
λRESL 0.297 0.078 0.023 0.19 0.087
NDVI 0.814 0.753 0.303 0.417 0.585
PRI 0.716 0.567 -0.016 0.243 0.029
RESL 0.579 0.637 0.215 0.149 0.172
REST 0.55 0.595 0.195 0.127 0.152
WBI1 -0.688 -0.587 -0.163 -0.641 -0.335
WBI2 -0.722 -0.504 -0.074 -0.534 -0.123
 Red-Edge indicators (Slope, Step, REMS) have bad correlation coefficientsRed-Edge indicators (Slope, Step, REMS) have bad correlation coefficients
compared to 2002 campaign. REST had 0.63 for GH and 0.68 for Biomass.compared to 2002 campaign. REST had 0.63 for GH and 0.68 for Biomass.
This difference is probably the consequence of the poor images quality resulting of badThis difference is probably the consequence of the poor images quality resulting of bad
meteorological conditions during the flight.meteorological conditions during the flight.

25. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral IndicesSpectral Indices
Relationship of the grass height and the step value of the
reflectance spectra in the red edge band
y = 0.0469x - 9.2337
R2
= 0.63
0
5
10
15
20
25
200 250 300 350 400 450 500 550 600 650 700
Red edge step
Grassheight(cm)
Relationship of the green weight and the step value of the
reflectance spectra in the red edge band
y = 37.858x - 10179
R2
= 0.6821
0
4000
8000
12000
16000
20000
200 250 300 350 400 450 500 550 600 650
Red edge step
Greenweight(kg/ha)
 Red-Edge Step:Red-Edge Step:

Height (R²=0.63)Height (R²=0.63)

Green weight (R²=0.68)Green weight (R²=0.68)
 Water Balance Index:Water Balance Index:

Dry matter content (R²=0.61)Dry matter content (R²=0.61)
Relationship of the water balance indice and the dry matter
content of grassland canopy
y = 61.984x - 11.559
R2
= 0.615
10
20
30
40
50
0.4 0.5 0.6 0.7 0.8 0.9
WBI2
Drymattercontent(MS%)

26. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Pixel levelSpectral analysis at Pixel level
 Red-edge indicators seems to be too sensible to the meteorologicalRed-edge indicators seems to be too sensible to the meteorological
conditions and can not be considered for an operational system.conditions and can not be considered for an operational system.
 Inspection of the regression results obtained in the 2002 and 2003Inspection of the regression results obtained in the 2002 and 2003
campaigns indicates that NDVI, WBI1 and PRI are the best indicatorscampaigns indicates that NDVI, WBI1 and PRI are the best indicators
to estimate biophysics characteristicsto estimate biophysics characteristics..
Confirm CASI-SASI 2002 results on biophysical parameters (FMY, DMY, GH)Confirm CASI-SASI 2002 results on biophysical parameters (FMY, DMY, GH)
directly linked to the age and the management practices supported bydirectly linked to the age and the management practices supported by
grasslands.grasslands.

27. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Parcel levelSpectral analysis at Parcel level
 In a second step, the project has analysed the possibility to classifyIn a second step, the project has analysed the possibility to classify
grassland in 3 management classes:grassland in 3 management classes:
• Haying grasslands (P)Haying grasslands (P)
• Grazing grasslands (F)Grazing grasslands (F)
• Neither haying nor grazing grasslands (NP NF)Neither haying nor grazing grasslands (NP NF)
 The grassland classification is based on the hypothesis that managementThe grassland classification is based on the hypothesis that management
practices can be identified by the combination ofpractices can be identified by the combination of
• Vegetation indices (Vegetation indices (quantitative parametersquantitative parameters) selected at the pixel) selected at the pixel
levellevel
• Textural indices (Textural indices (qualitative parametersqualitative parameters))
 Different textural indices were calculated:Different textural indices were calculated:
• Global approach (global variance)Global approach (global variance)
• Local approach (moving windows of 3x3 pixels)Local approach (moving windows of 3x3 pixels)

28. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Parcel levelSpectral analysis at Parcel level
 Step 1:Step 1: DDiscriminant analysis to identify regions of interest in the reflectanceiscriminant analysis to identify regions of interest in the reflectance
spectra and to choose the relevant textural indicesspectra and to choose the relevant textural indices
Probability level of the differences between grassland classes
Global texture parameterGlobal texture parameter
=> 2 regions of interest=> 2 regions of interest
- Below 500 nmBelow 500 nm
- Above 725 nm.Above 725 nm.
Local textureLocal texture
=> Quite different
- Around 500 nm
- Above 750 nm
- BUT observed level are
not significant (P<0.05).

29. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral analysis at Parcel levelSpectral analysis at Parcel level
 Step 1:Step 1: Based on these results, 2 wave bands from the global texture
analysis have been selected (450 nm and 725 nm) together with
previously defined vegetation indices.
 Step 2:Step 2: Discriminant analysis with all the selected dependent variables
(vegetation indicators & textural indicators)
• The stepwise discriminant analysis identified 3 vegetation indices,
(PRI, NDVI and WBI1) and one textural global index (450nm) as
significant.
• Cross validation procedure show that about 83% of correct
classifications may be obtained.

30. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Results: Cross classificationResults: Cross classification
 All the parcels with AEM are well identified MCo = MCi = NP NFAll the parcels with AEM are well identified MCo = MCi = NP NF
 Only 4 parcels have been classified in a bad management classOnly 4 parcels have been classified in a bad management class
(MCo(MCo ≠≠ MCi).MCi).
Classification
results
Observed management classes
F NP NF P
F 13 0 1
NP NF 2 3 1
P 0 0 4
N total 15 3 6
N correct 13 3 4
Proportion 86,70% 100% 66,70%

31. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Results: Cross classificationResults: Cross classification
 Only 4 parcels have been classified in a bad management classOnly 4 parcels have been classified in a bad management class
2 parcel with2 parcel with MCo = FMCo = F classified inclassified in MCi = NP NFMCi = NP NF
1 parcel with1 parcel with MCo = PMCo = P classified inclassified in MCi = NP NFMCi = NP NF
1 parcel with1 parcel with MCo = PMCo = P withwith MCi = FMCi = F
These misclassifications can easily be explained by a regrowth of grass after
a long period without pasture or after haying.
 These results also show that if the 24 parcels had been declared inThese results also show that if the 24 parcels had been declared in
AEM, and 18 parcelsAEM, and 18 parcels of them would be irregular (MCoof them would be irregular (MCo ≠ NP NF),≠ NP NF),
only 3 parcels would not have been identified as irregular by remoteonly 3 parcels would not have been identified as irregular by remote
sensing (MCi = NP NF).sensing (MCi = NP NF).

32. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Spectral IndicesSpectral Indices (cont.)(cont.)
 Red-Edge Step & Water Balance Index can be used toRed-Edge Step & Water Balance Index can be used to
discriminate between grassland management typesdiscriminate between grassland management types
 Chemical characteristic of grass was not clearly linked toChemical characteristic of grass was not clearly linked to
vegetation indices.vegetation indices.
 Except for VEM and VEVI energy values which seem wellExcept for VEM and VEVI energy values which seem well
correlated with NDVI (R²=0.60 )correlated with NDVI (R²=0.60 )
 In most of cases results from scissors cutting samples whichIn most of cases results from scissors cutting samples which
represent the upper part of the canopy were best correlated withrepresent the upper part of the canopy were best correlated with
vegetation indices.vegetation indices.

33. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Multiple regression analysisMultiple regression analysis
 CASI sensor = best resultsCASI sensor = best results
 CASI + SASI = not better predictionCASI + SASI = not better prediction
 Good predictive quality in generalGood predictive quality in general
Predictive quality of the models
Comparison of the different sensor data
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
D
ry
m
atter
P
rotein
C
ellu
lose
S
ugar
V
EM
V
EV
I
D
V
E
O
E
B
H
eight
B
iom
ass
W
etm
atter
Grass characteristic
R2

34. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
Multiple regression analysisMultiple regression analysis
Predictive quality of the models
Comparison of the two cutting systems
0
10
20
30
40
50
60
70
80
Dry
matter
Protein Cellulose Sugar VEM VEVI DVE OEB
Grass characteristic
Relativerootmeansquareerror
Scissors
Mower
 RMSE scissors lower than mowerRMSE scissors lower than mower
 RMSERMSE ≤≤ 10% => good predictions10% => good predictions
 Except for OEB & SugarExcept for OEB & Sugar

35. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
ConclusionsConclusions
Higher potential
Lower potential
< 1000 kg/ha
> 15 000 kg/ha
Estimation of the wet matter
production at regional level.
 Question 1:Question 1:
Regional monitoring?Regional monitoring?

36. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
ConclusionsConclusions
 Question 1:Question 1:
Regional monitoring.Regional monitoring.
 Question 2:Question 2:
grassland discrimination?grassland discrimination?
Pure ray-grass not yet harvested
Just mowed
Just pastured
Regrown

37. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
ConclusionsConclusions
 Question 1:Question 1:
Prediction.Prediction.
 Question 2:Question 2:
grassland discrimination.grassland discrimination.
 Question n°3:Question n°3:
Management practices?Management practices?
Just pastured
Not yet pastured

38. Airborne Imaging
Spectroscopy Workshop –
Bruges, October, 8 2004
ConclusionsConclusions
 These study assess the ability of Imaging Spectroscopy to be reliableThese study assess the ability of Imaging Spectroscopy to be reliable
method for estimating grassland management practices and to control ifmethod for estimating grassland management practices and to control if
AEM are correctly applied.AEM are correctly applied.
 3 vegetation indices (PRI, NDVI and WBI13 vegetation indices (PRI, NDVI and WBI1 ) are confirmed as goodare confirmed as good
quantitative parametersquantitative parameters
 Textural indices (qualitative parameters) and vegetation indicesTextural indices (qualitative parameters) and vegetation indices
(quantitative parameters ) are linked to the age and the management(quantitative parameters ) are linked to the age and the management
practices supported by grasslandspractices supported by grasslands
 Changes of spectral signature depend not only on the nature and theChanges of spectral signature depend not only on the nature and the
intensity of the action, but also on the time spent between the action andintensity of the action, but also on the time spent between the action and
the remote sensing data acquisition.the remote sensing data acquisition.
 Classification results are promising and must be validated with betterClassification results are promising and must be validated with better
images quality due to the bad meteorological conditions.images quality due to the bad meteorological conditions.